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1.西南交通大学电气工程学院,四川省成都市 611756
2.国网四川省电力公司经济技术研究院,四川省成都市 610041
Received:14 June 2023,
Revised:2023-08-08,
Published Online:04 February 2024,
Published:10 April 2024
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杨帅,戴朝华,郭爱等.考虑负荷空间均衡和充电站合作博弈的快速充电定价机制[J].电力系统自动化,2024,48(7):190-200.
YANG Shuai,DAI Chaohua,GUO Ai,et al.Fast Charging Pricing Mechanism Considering Load Space Balance and Cooperative Game of Charging Stations[J].Automation of Electric Power Systems,2024,48(7):190-200.
杨帅,戴朝华,郭爱等.考虑负荷空间均衡和充电站合作博弈的快速充电定价机制[J].电力系统自动化,2024,48(7):190-200. DOI: 10.7500/AEPS20230614001.
YANG Shuai,DAI Chaohua,GUO Ai,et al.Fast Charging Pricing Mechanism Considering Load Space Balance and Cooperative Game of Charging Stations[J].Automation of Electric Power Systems,2024,48(7):190-200. DOI: 10.7500/AEPS20230614001.
为了平衡快速充电负荷空间分布,提高电动汽车用户充电满意度,提出考虑负荷空间均衡和充电站合作博弈的快速充电动态定价机制。首先,通过实际订单数据逆地理编码得到出行概率转移矩阵,结合路网模型和速度-流量模型建立电动汽车充电负荷时空分布模型;然后,考虑用户主观意愿,建立计及用户偏好的充电站选择决策模型,模拟用户对于充电站选择的理性决策行为;最后,以充电站节点负荷均衡性为目标,建立区域内快速充电站合作模式下的动态定价博弈模型,通过迭代算法求解均衡值,并利用Shapley值法对合作联盟所得剩余收益进行分配。仿真实例表明
所提快速充电站动态定价策略能有效降低充电站节点的负荷方差,提高充电桩利用率和充电站收益,并减小用户排队时间,提高用户满意度。
In order to balance the spatial distribution of fast charging load and improve electric vehicle (EV) users’ charging satisfaction
this paper proposes a dynamic pricing mechanism for fast charging considering the load space balance and the cooperation game of charging stations. First
the travel probability transfer matrix is obtained through inverse geocoding of the actual order data
and the spatial and temporal distribution model of EV charging load is established by combining the road network model and the speed-flow model. Then
the charging station selection decision-making model is established by considering the users’ subjective willingness to take into account users’ preferences
and the rational decision-making behaviors of the users’ charging station selection are simulated. Finally
the dynamic pricing game model in the cooperation mode of fast charging stations in the region is established with the objective of charging station node load balance
and the equilibrium value is solved by an iterative algorithm
while the residual benefits obtained from the cooperative alliance are distributed by using the Shapley value method. The simulation example shows that the proposed dynamic pricing strategy can effectively reduce the load variance of charging station nodes
improve the utilization rate of charging piles and charging station revenues
and reduce the queuing time of users to improve users’ satisfaction.
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